Unmanned aerial vehicle airborne antenna attitude stability monitoring and early warning method and system

Abstract

The invention discloses an unmanned aerial vehicle airborne antenna attitude stability monitoring and early warning method and system, and belongs to the technical field of antenna pointing stability. According to the invention, the real-time body attitude data and the communication signal intensity data of the unmanned aerial vehicle are combined to form the time sequence aligned data pair, and the expected signal intensity and the confidence interval corresponding to the current attitude are obtained based on the body attitude data and the preset attitude signal intensity mapping model; the expected signal strength is compared with the communication signal strength data to generate a first early warning signal, a future attitude trajectory is predicted based on the body attitude data and an unmanned aerial vehicle kinematics model, and a predicted antenna pointing deviation angle sequence is solved and compared with a dynamic risk threshold to generate a second early warning signal; real-time comprehensive monitoring of attitude stability and communication signal quality of the airborne antenna of the unmanned aerial vehicle is realized, and the problem that monitoring and early warning of the pointing stability of the antenna are not accurate and timely enough in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of antenna pointing stabilization, and in particular to a method and system for monitoring and early warning of the attitude stabilization of an antenna onboard a UAV. Background Art

[0002] With the rapid development of drone technology, it has found widespread application in fields such as communications, surveying and mapping, and logistics. In these applications, the stability and reliability of drone antennas are crucial. Antenna stability directly impacts the signal quality of the communication link, which in turn affects drone flight safety and mission performance.

[0003] Existing technologies, such as Chinese Patent Publication No. CN113690617A, describe a control method and system for a phased array antenna module. This method obtains the satellite signal strength received by the phased array antenna module. When the signal strength falls below a preset threshold, it estimates the current estimated attitude angle based on the antenna's historical attitude angles and adjusts the antenna module's attitude angle to maintain signal reception quality. Another example is Chinese Patent Publication No. CN116147572A, which describes an IoT-based antenna attitude monitoring and early warning system. This system monitors antenna attitude data during operation, identifies the antenna's status, and issues level-based early warnings for abnormal conditions.

[0004] Existing technologies only passively adjust antenna attitude when signal strength becomes abnormal, but cannot actively monitor attitude stability or provide early warning of attitude deviations, effectively preventing the risk of communication link interruption. While the latter can monitor antenna attitude data, it struggles to meet the high-precision attitude monitoring and early warning requirements of drones in complex flight environments. Neither approach offers real-time, accurate, and forward-looking comprehensive monitoring and early warning of drone-mounted antenna attitude stability and communication signal quality, resulting in insufficiently accurate and timely monitoring and early warning of antenna pointing stability. Summary of the Invention

[0005] The embodiments of the present application provide a method and system for monitoring and early warning of the attitude stability of an onboard antenna of a UAV, thereby solving the problem of insufficient accuracy and timeliness in monitoring and early warning of antenna pointing stability in the prior art, and realizing real-time comprehensive monitoring of the attitude stability of the onboard antenna of a UAV and the quality of the communication signal.

[0006] The present invention provides a method for monitoring and warning the stability of an onboard antenna of a UAV, including:

[0007] Acquire and synchronize the drone's real-time body attitude data with the communication signal strength data from the onboard antenna to form a time-aligned attitude signal data pair;

[0008] Based on the body posture data and the preset posture signal strength mapping model, the expected signal strength and confidence interval corresponding to the current posture are obtained, the expected signal strength is compared with the communication signal strength data, and a first warning signal is generated;

[0009] Based on the body attitude data and the UAV kinematic model, the predicted attitude trajectory within a certain time window in the future is predicted. According to the predicted attitude trajectory and the ground station position coordinates, the predicted antenna pointing deviation angle sequence is solved. The predicted antenna pointing deviation angle sequence is compared with the dynamic risk threshold to generate a second warning signal;

[0010] Based on the attitude signal data pairs, the attitude signal strength mapping model is updated online;

[0011] The first warning signal and the second warning signal are integrated to output the final warning instruction.

[0012] Furthermore, the method of obtaining the expected signal strength and confidence interval corresponding to the current posture based on the body posture data and the preset posture signal strength mapping model specifically includes:

[0013] Get the body posture data at the current time t ,in is the roll angle, is the pitch angle, is the yaw angle;

[0014] Input the body posture data into the preset posture signal strength mapping model to obtain the expected signal strength and the variance of the predicted distribution;

[0015] The attitude signal strength mapping model The construction process is:

[0016] Obtain historical posture signal data pairs and use Gaussian process regression to establish a nonlinear mapping model. The nonlinear mapping model is:

[0017] ;

[0018] In the formula, is the mean function, is the covariance function, which is used to characterize the correlation between the signal intensities corresponding to different posture points P and P';

[0019] Any body posture data Substitute into the nonlinear mapping model:

[0020] ;

[0021] Get the expected signal strength and the variance of the predicted distribution;

[0022] Get confidence intervals based on the variance.

[0023] Furthermore, the step of comparing the expected signal strength with the communication signal strength data to generate a first warning signal specifically includes:

[0024] Get the communication signal strength data measured at time t ;

[0025] According to the expected signal strength and variance , calculate the standardized residual of the signal intensity deviation:

[0026] ;

[0027] Determine whether the standardized residual meets the warning conditions: ,in, is the preset confidence interval coefficient;

[0028] If the condition is met, the first warning signal is generated.

[0029] Furthermore, the predicted attitude trajectory within a certain time window in the future is predicted based on the body attitude data and the UAV kinematic model, specifically including:

[0030] Get the attitude quaternion at the current time t and the body angular velocity vector ;

[0031] By numerically integrating the UAV kinematic model, the prediction is Attitude quaternion at the moment :

[0032] ;

[0033] in, represents quaternion multiplication, It is a pure quaternion composed of the body's angular velocity vector. is the prediction time step.

[0034] Furthermore, the method further calculates a predicted antenna pointing deviation angle sequence based on the predicted attitude trajectory and the ground station position coordinates, specifically including:

[0035] The predicted attitude quaternion Convert to rotation matrix ;

[0036] The nominal pointing vector of the antenna in the body coordinate system is converted by this rotation matrix Convert to the navigation coordinate system and get the predicted pointing vector ;

[0037] Based on the current position coordinates of the drone and ground station position coordinates , calculate the sight vector in the navigation coordinate system:

[0038] ;

[0039] in, is the position vector from the UAV to the ground station;

[0040] Calculate the predicted pointing vector and the sight vector pointing to the ground station in the navigation coordinate system The angle between them is used as the predicted antenna pointing deviation angle :

[0041] ;

[0042] in, is the nominal pointing vector of the antenna attached to the aircraft body, It is the vector pointing from the current position of the UAV to the ground station.

[0043] Furthermore, the step of comparing the predicted antenna pointing deviation angle sequence with the dynamic risk threshold specifically includes:

[0044] The dynamic risk threshold at the current time t The calculation method is:

[0045] ;

[0046] in, is the basic static risk threshold, for The angular acceleration vector at time , represents the L2 norm, is the integration time window.

[0047] Furthermore, the generating of the second warning signal is specifically:

[0048] If in the prediction time window At any moment The predicted antenna pointing deviation angle satisfy , then the second warning signal is generated.

[0049] Furthermore, the online updating of the attitude signal strength mapping model according to the attitude signal data pair specifically includes:

[0050] Use the newly collected gesture signal data pair as new evidence ;

[0051] Use Bayesian inference method to update the hyperparameter set of the attitude signal strength mapping model ;

[0052] The posterior probability density function of the hyperparameter is: ;

[0053] Update the hyperparameters by maximizing the posterior probability.

[0054] Furthermore, the fusing of the first warning signal and the second warning signal to output a final warning instruction specifically includes:

[0055] Defining the First Warning Signal and the second warning signal , where 0 means no warning, and 1 means triggering a warning;

[0056] Final warning level Determined by the following logic:

[0057] ;

[0058] when When the signal quality is abnormal, a first-level warning instruction is output;

[0059] when When the device is in the state of emergency, it will output a second-level warning instruction to indicate that the posture is pointing to a risk;

[0060] when When a third-level warning instruction is output, it indicates that the communication link is at serious risk;

[0061] The warning instruction includes the warning level, trigger type identifier, trigger time stamp, current posture signal data snapshot And the predicted antenna pointing deviation angle value.

[0062] The present application provides a UAV airborne antenna attitude stability monitoring and early warning system, which is used to implement a UAV airborne antenna attitude stability monitoring and early warning method, including:

[0063] The data pair acquisition module is used to acquire and synchronize the real-time body attitude data of the UAV with the communication signal strength data of the airborne antenna to form a time-aligned attitude signal data pair;

[0064] A first warning module is configured to obtain an expected signal strength and a confidence interval corresponding to a current posture based on the body posture data and a preset posture signal strength mapping model, compare the expected signal strength with the communication signal strength data, and generate a first warning signal;

[0065] The second warning module is used to predict the predicted attitude trajectory within a certain time window in the future based on the body attitude data and the UAV kinematic model, and calculate the predicted antenna pointing deviation angle sequence based on the predicted attitude trajectory and the ground station position coordinates. The predicted antenna pointing deviation angle sequence is compared with the dynamic risk threshold to generate a second warning signal;

[0066] An online update module, used for updating the attitude signal strength mapping model online according to the attitude signal data pair;

[0067] The early warning output module is used to fuse the first early warning signal and the second early warning signal and output the final early warning instruction.

[0068] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0069] 1. By acquiring and synchronizing the real-time posture data of the drone and the communication signal strength data of the airborne antenna, a time-aligned posture signal data pair is formed, thereby establishing a dynamic association between posture and signal strength. This enables real-time comprehensive monitoring of the posture stability and communication signal quality of the drone's airborne antenna, effectively solving the problem of inaccurate and timely monitoring and early warning of antenna pointing stability in existing technologies.

[0070] 2. By obtaining the expected signal strength and confidence interval corresponding to the current posture based on the body posture data and the preset posture signal strength mapping model, and comparing the expected signal strength with the communication signal strength data to generate the first warning signal, the system actively monitors abnormal fluctuations in signal strength under the current posture, thereby achieving early warning of the risk of communication link interruption.

[0071] 3. By predicting the future attitude trajectory based on the body attitude data and the UAV kinematic model, the predicted antenna pointing deviation angle sequence is calculated and compared with the dynamic risk threshold to generate a second warning signal, thereby proactively assessing the antenna pointing deviation risk and achieving early prevention of potential communication link problems. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] Figure 1 This is a diagram of the architecture of a method for monitoring and warning the stability of an onboard antenna of a UAV provided in an embodiment of the present application;

[0073] Figure 2 A structural diagram of a UAV airborne antenna attitude stabilization monitoring and early warning system provided in an embodiment of the present application. DETAILED DESCRIPTION

[0074] The embodiments of the present application solve the problem of inaccurate and timely monitoring and warning of antenna pointing stability in the prior art by providing a method and system for monitoring and warning of the attitude stability of an onboard antenna of a drone. By acquiring and synchronizing the real-time body attitude data of the drone and the communication signal strength data of the onboard antenna, a time-aligned attitude signal data pair is formed, thereby establishing a dynamic association between attitude and signal strength, and realizing real-time comprehensive monitoring of the attitude stability and communication signal quality of the onboard antenna of the drone.

[0075] In order to better understand the above technical solution, the above technical solution will be described in detail below with reference to the accompanying drawings and specific implementation methods.

[0076] like Figure 1 As shown, an embodiment of the present application provides a method for monitoring and warning the stability of an airborne antenna of a UAV. The method is applied to a monitoring and warning system for monitoring and warning the stability of an airborne antenna of a UAV, including:

[0077] Acquire and synchronize the drone's real-time body attitude data with the communication signal strength data from the onboard antenna to form a time-aligned attitude signal data pair;

[0078] Based on the body posture data and the preset posture signal strength mapping model, the expected signal strength and confidence interval corresponding to the current posture are obtained, the expected signal strength is compared with the communication signal strength data, and a first warning signal is generated;

[0079] Based on the body attitude data and the UAV kinematic model, the predicted attitude trajectory within a certain time window in the future is predicted. According to the predicted attitude trajectory and the ground station position coordinates, the predicted antenna pointing deviation angle sequence is solved. The predicted antenna pointing deviation angle sequence is compared with the dynamic risk threshold to generate a second warning signal;

[0080] Based on the attitude signal data pairs, the attitude signal strength mapping model is updated online;

[0081] The first warning signal and the second warning signal are integrated to output the final warning instruction.

[0082] Furthermore, the method of obtaining the expected signal strength and confidence interval corresponding to the current posture based on the body posture data and the preset posture signal strength mapping model specifically includes:

[0083] Get the body posture data at the current time t ,in is the roll angle, is the pitch angle, is the yaw angle;

[0084] Input the body posture data into the preset posture signal strength mapping model to obtain the expected signal strength and the variance of the predicted distribution;

[0085] The attitude signal strength mapping model The construction process is:

[0086] Obtain historical posture signal data pairs and use Gaussian process regression to establish a nonlinear mapping model. The nonlinear mapping model is:

[0087] ;

[0088] In the formula, is the mean function, is the covariance function, which is used to characterize the correlation between the signal intensities corresponding to different posture points P and P';

[0089] Any body posture data Substitute into the nonlinear mapping model:

[0090] ;

[0091] Get the expected signal strength and the variance of the prediction distribution ;

[0092] Calculated based on variance Confidence Interval:

[0093] ;

[0094] in is the quantile of the standard normal distribution.

[0095] Furthermore, the step of comparing the expected signal strength with the communication signal strength data to generate a first warning signal specifically includes:

[0096] Get the communication signal strength data measured at time t ;

[0097] According to the expected signal strength and variance , calculate the standardized residual of the signal intensity deviation:

[0098] ;

[0099] Determine whether the standardized residual meets the warning conditions: ,in, is the preset confidence interval coefficient, which represents the sensitivity of the early warning;

[0100] If the condition is met, the first warning signal is generated;

[0101] described The value range of is 2.0-3.0, corresponding to a confidence level of 95%-99.7%.

[0102] Furthermore, the predicted attitude trajectory within a certain time window in the future is predicted based on the body attitude data and the UAV kinematic model, specifically including:

[0103] Get the attitude quaternion at the current time t and the body angular velocity vector ;

[0104] By numerically integrating the UAV kinematic model, the prediction is Attitude quaternion at the moment :

[0105] ;

[0106] in, represents quaternion multiplication, It is a pure quaternion composed of the body's angular velocity vector. is the prediction time step.

[0107] Furthermore, the method further calculates a predicted antenna pointing deviation angle sequence based on the predicted attitude trajectory and the ground station position coordinates, specifically including:

[0108] The predicted attitude quaternion Convert to rotation matrix ;

[0109] The nominal pointing vector of the antenna in the body coordinate system is converted by this rotation matrix Convert to the navigation coordinate system and get the predicted pointing vector ;

[0110] Based on the current position coordinates of the drone and ground station position coordinates , calculate the sight vector in the navigation coordinate system:

[0111] ;

[0112] in, is the position vector from the UAV to the ground station;

[0113] Calculate the predicted pointing vector and the sight vector pointing to the ground station in the navigation coordinate system The angle between them is used as the predicted antenna pointing deviation angle :

[0114] ;

[0115] in, is the nominal pointing vector of the antenna attached to the aircraft body, It is the vector pointing from the current position of the UAV to the ground station, which can be calculated from GPS data.

[0116] Furthermore, the step of comparing the predicted antenna pointing deviation angle sequence with the dynamic risk threshold specifically includes:

[0117] The dynamic risk threshold at the current time t The calculation method is:

[0118] ;

[0119] in, is the basic static risk threshold, for The angular acceleration vector at the moment is obtained by taking the derivative of the angular velocity, represents the L2 norm, The integration time window is used to quantify the intensity of the UAV's maneuvering by integrating the recent angular acceleration. The more intense the maneuvering, the greater the risk threshold.

[0120] Furthermore, the generating of the second warning signal is specifically:

[0121] If in the prediction time window At any moment The predicted antenna pointing deviation angle satisfy , then the second warning signal is generated.

[0122] Furthermore, the online updating of the attitude signal strength mapping model according to the attitude signal data pair specifically includes:

[0123] Use the newly collected gesture signal data pair as new evidence ;

[0124] Use Bayesian inference method to update the hyperparameter set of the attitude signal strength mapping model ;

[0125] The posterior probability density function of the hyperparameter is: ;

[0126] Update hyperparameters by maximizing the posterior probability;

[0127] Its posterior probability Proportional to the likelihood function Prior probability with previous hyperparameters The product of , realizes the online learning and adaptation of the model;

[0128] in, Represents historical data, Represents newly collected data, is a set of model hyperparameters, including the length scale of the covariance function, signal variance, etc.

[0129] Furthermore, the fusing of the first warning signal and the second warning signal to output a final warning instruction specifically includes:

[0130] Defining the First Warning Signal and the second warning signal , where 0 means no warning, and 1 means triggering a warning;

[0131] Final warning level Determined by the following logic:

[0132] ;

[0133] when When the signal quality is abnormal, a first-level warning instruction is output;

[0134] when When the device is in the state of emergency, it will output a second-level warning instruction to indicate that the posture is pointing to a risk;

[0135] when When a third-level warning instruction is output, it indicates that the communication link is at serious risk;

[0136] The warning instruction includes the warning level, trigger type identifier, trigger time stamp, current posture signal data snapshot And the predicted antenna pointing deviation angle value.

[0137] like Figure 2 As shown, the embodiment of the present application provides a UAV airborne antenna attitude stability monitoring and early warning system, which is used to implement the UAV airborne antenna attitude stability monitoring and early warning method, including: a data pair acquisition module, a first early warning module, a second early warning module, an online update module, and an early warning output module;

[0138] The data pair acquisition module is used to acquire and synchronize the real-time body attitude data of the UAV and the communication signal strength data of the airborne antenna to form a time-aligned attitude signal data pair;

[0139] The first warning module is configured to obtain an expected signal strength and a confidence interval corresponding to a current posture based on the body posture data and a preset posture signal strength mapping model, compare the expected signal strength with the communication signal strength data, and generate a first warning signal;

[0140] The second warning module is used to predict the predicted attitude trajectory within a certain time window in the future based on the body attitude data and the UAV kinematic model, and calculate the predicted antenna pointing deviation angle sequence based on the predicted attitude trajectory and the ground station position coordinates, and generate a second warning signal based on the comparison of the predicted antenna pointing deviation angle sequence with the dynamic risk threshold;

[0141] The online update module is used to update the attitude signal strength mapping model online according to the attitude signal data pair;

[0142] The warning output module is used to fuse the first warning signal and the second warning signal and output a final warning instruction.

[0143] In summary, the embodiments of the present application form a time-aligned data pair by combining the real-time body attitude data of the drone and the communication signal strength data, and combine the attitude signal strength mapping model with the drone kinematic model to achieve active monitoring of the current signal strength, prediction and warning of antenna pointing deviation under future attitude trajectory, and online update of the model, thereby effectively preventing the risk of communication link interruption, meeting the drone's needs for high-precision attitude monitoring and warning in complex flight environments, and improving the monitoring accuracy and timeliness of warning of antenna attitude stability and communication signal quality.

[0144] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0145] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0146] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0147] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0148] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0149] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0150] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A method for monitoring and warning the stability of an aerial vehicle's airborne antenna, characterized in that: The following steps are involved: Acquire and synchronize the drone's real-time body attitude data with the communication signal strength data from the onboard antenna to form a time-aligned attitude signal data pair; Based on the body posture data and the preset posture signal strength mapping model, the expected signal strength and confidence interval corresponding to the current posture are obtained, the expected signal strength is compared with the communication signal strength data, and a first warning signal is generated; Based on the body attitude data and the UAV kinematic model, the predicted attitude trajectory within a certain time window in the future is predicted. According to the predicted attitude trajectory and the ground station position coordinates, the predicted antenna pointing deviation angle sequence is solved. The predicted antenna pointing deviation angle sequence is compared with the dynamic risk threshold to generate a second warning signal; Based on the attitude signal data pairs, the attitude signal strength mapping model is updated online; The first warning signal and the second warning signal are integrated to output the final warning instruction.

2. A method for monitoring and warning the stability of an airborne antenna of a UAV as claimed in claim 1, characterized in that: The method of obtaining the expected signal strength and confidence interval corresponding to the current posture based on the body posture data and the preset posture signal strength mapping model specifically includes: Get the body posture data at the current time t ,in is the roll angle, is the pitch angle, is the yaw angle; Input the body posture data into the preset posture signal strength mapping model to obtain the expected signal strength and the variance of the predicted distribution; The attitude signal strength mapping model The construction process is: Obtain historical posture signal data pairs and use Gaussian process regression to establish a nonlinear mapping model. The nonlinear mapping model is: ； In the formula, is the mean function, is the covariance function, which is used to characterize the correlation between the signal intensities corresponding to different posture points P and P'; Any body posture data Substitute into the nonlinear mapping model: ； Get the expected signal strength and the variance of the predicted distribution; Get confidence intervals based on the variance.

3. The method for monitoring and warning the attitude stability of an airborne antenna of a UAV as claimed in claim 2, characterized in that: The step of comparing the expected signal strength with the communication signal strength data to generate a first warning signal specifically includes: Get the communication signal strength data measured at time t ; According to the expected signal strength and variance , calculate the standardized residual of the signal intensity deviation: ； Determine whether the standardized residual meets the warning conditions: ,in, is the preset confidence interval coefficient; If the condition is met, the first warning signal is generated.

4. The method for monitoring and warning the attitude stability of an aerial vehicle mounted on a UAV as claimed in claim 1, characterized in that: The predicted attitude trajectory within a certain time window in the future is predicted based on the body attitude data and the UAV kinematic model, specifically including: Get the attitude quaternion at the current time t and the body angular velocity vector ; By numerically integrating the UAV kinematic model, the prediction is Attitude quaternion at the moment : ； in, represents quaternion multiplication, It is a pure quaternion composed of the body's angular velocity vector. is the prediction time step.

5. A method for monitoring and warning the stability of an aerial vehicle's airborne antenna posture as claimed in claim 4, characterized in that: The method of calculating the predicted antenna pointing deviation angle sequence based on the predicted attitude trajectory and the ground station position coordinates specifically includes: The predicted attitude quaternion Convert to rotation matrix ; The nominal pointing vector of the antenna in the body coordinate system is converted by this rotation matrix Convert to the navigation coordinate system and get the predicted pointing vector ; Based on the current position coordinates of the drone and ground station position coordinates , calculate the sight vector in the navigation coordinate system: ； in, is the position vector from the UAV to the ground station; Calculate the predicted pointing vector and the sight vector pointing to the ground station in the navigation coordinate system The angle between them is used as the predicted antenna pointing deviation angle : ； in, is the nominal pointing vector of the antenna attached to the aircraft body, It is the vector pointing from the current position of the UAV to the ground station.

6. The method for monitoring and warning the attitude stability of an aerial vehicle mounted on a UAV as claimed in claim 1, characterized in that: The comparing the predicted antenna pointing deviation angle sequence with the dynamic risk threshold specifically includes: The dynamic risk threshold at the current time t The calculation method is: ； in, is the basic static risk threshold, for The angular acceleration vector at time , represents the L2 norm, is the integration time window.

7. A method for monitoring and warning the stability of an airborne antenna of a UAV as claimed in claim 6, characterized in that: The generating of the second warning signal is specifically: If in the prediction time window At any moment The predicted antenna pointing deviation angle satisfy , then the second warning signal is generated.

8. The method for monitoring and warning the attitude stability of an aerial vehicle mounted on a UAV as claimed in claim 1, characterized in that: The online updating of the attitude signal strength mapping model according to the attitude signal data pair specifically includes: Use the newly collected gesture signal data pair as new evidence ; Use Bayesian inference method to update the hyperparameter set of the attitude signal strength mapping model ; The posterior probability density function of the hyperparameter is: ; Update the hyperparameters by maximizing the posterior probability.

9. The method for monitoring and warning the attitude stability of an airborne antenna of a UAV as claimed in claim 1, characterized in that: The fusing of the first warning signal and the second warning signal to output a final warning instruction specifically includes: Defining the First Warning Signal and the second warning signal , where 0 means no warning, and 1 means triggering a warning; Final warning level Determined by the following logic: ； when When the signal quality is abnormal, a first-level warning instruction is output; when When the device is in the state of emergency, it will output a second-level warning instruction to indicate that the posture is pointing to a risk; when When a third-level warning instruction is output, it indicates that the communication link is at serious risk; The warning instruction includes the warning level, trigger type identifier, trigger time stamp, current posture signal data snapshot And the predicted antenna pointing deviation angle value.

10. A UAV airborne antenna attitude stability monitoring and early warning system, used to implement the UAV airborne antenna attitude stability monitoring and early warning method according to any one of claims 1 to 9, characterized in that: include: The data pair acquisition module is used to acquire and synchronize the real-time body attitude data of the UAV with the communication signal strength data of the airborne antenna to form a time-aligned attitude signal data pair; A first warning module is configured to obtain an expected signal strength and a confidence interval corresponding to a current posture based on the body posture data and a preset posture signal strength mapping model, compare the expected signal strength with the communication signal strength data, and generate a first warning signal; The second warning module is used to predict the predicted attitude trajectory within a certain time window in the future based on the body attitude data and the UAV kinematic model, and calculate the predicted antenna pointing deviation angle sequence based on the predicted attitude trajectory and the ground station position coordinates. The predicted antenna pointing deviation angle sequence is compared with the dynamic risk threshold to generate a second warning signal; An online update module, used for updating the attitude signal strength mapping model online according to the attitude signal data pair; The early warning output module is used to fuse the first early warning signal and the second early warning signal and output the final early warning instruction.

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